Seattle code camp 2016 - Role of Data Science in Healthcare
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Everyone loves to shake a stick at the healthcare industry for being backward. Fact is there is no lack of technology or data in healthcare. Biggest challenge for healthcare providers is to identify what questions to ask the data. My team has implemented over 75 enterprise data warehouse projects in US healthcare industry. At the annual Seattle Code Camp, we discussed some of the examples of how data is used in the healthcare industry for compliance reporting (BI) and predictive analytics. These slides are from Seattle Code Camp 2016, shares technologies, concepts and ideas for data science in the US healthcare industry.
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Seattle code camp 2016 - Role of Data Science in Healthcare
- 1. DATA SCIENCE IN HEALTHCARE Seattle Code Camp 2016 1
- 2. ABOUT THE SPEAKER 2 Gaurav Garg “GG” is a business savvy IT leader playing the role of trusted advisor and product development manager. • Leads a Healthcare consulting practice for a national IT consulting firm (www.calance.com). • Participated in product strategy team - identified High Value Questions resulting in 150+ new products and features in $16B GE Healthcare product portfolio. • Played the role of IT Director at UCLA Health System, acting as an interface between the Clinical Directors and the IT Department. • Led the delivery of 8 products and 70+ Enterprise Data Warehouse projects in the Healthcare industry. • P&L responsibility managing application development up to $45MM. • Presented and won Phase I, Phase II grants from the NIH under SBIR funding to build a product in partnership with UCLA Health System. • As a thought leader, GG publishes white papers, appears in speaking engagements and guides healthcare startups at Cambia Grove. Gaurav Garg (GG) https://www.linkedin.com/in/gauravkinatus@gaurav_kinatus
- 4. HEALTHCARE BASICS Technologies & Flow of Information 4
- 5. WHY IS HEALTHCARE DATA MESSY? 5 Integrated Clinical Information System Financial System Lab Anatomic Pathology (6 vendors) EMPI (5 vendors) Dictation (4 vendors) Interface Engines (7 vendors) Ambulatory Specialty (4 vendors) Hospice (5 vendors) Advanced Visualization (5 vendors) Budgeting (3 vendors) Labor and Delivery (7 vendors) Time and Attendance (3 vendors) Ambulatory EMR (Small practice) (18 vendors) Anesthesia (1 vendor) PACS (31 vendors) Patient Monitoring System (1 vendor) Laboratory Blood Bank (9 vendors) CT Ultrasound Other medical Devices Ventilators Fetal monitors Core Measures Rapid Response PHR integration Ambulatory Inpatient Throughput Tracking Infection Tracking Quality Scorecard Care Quality Analysis Charge & Payment Analysis IHI Triggers
- 6. BI & DATA WAREHOUSE EXAMPLES 6 Clinical and Syndrome Surveillance MEWS; HAC Risk Scoring (DVT, DU, CLBSI, CA-UTI, CTI, PU, Wound); Sepsis; school absenteeism; bio-surveillance 1 Patient 1 Chart Optimizing Resource Utilization Imaging Tracking; Room Utilization; Capital Investment Optimization; Payer Compliance; Leakage; LOS; Re-Admissions Manager (RAM) Improving Physician Satisfaction Protocol Compliance Tracking Pharmacy Protocol, Core Measures (AMI, HF, PN, SCIP, MU), Nurse Admission Tracking Improving Patient Satisfaction Data explosion from EMR adaption has created lots of data. Calance has implemented over data warehouse projects in over 70 hospitals and HIEs. • EMPI Integration – we have experience integrating with popular EMPI solutions and troubleshooting performance issues. • Enterprise Interface Engine – implementation, upgrade, migrate, configure interfaces. Caradigm Intelligence Platform (aka Amalga), Orion Rhapsody, eGate, Intersystem Ensemble, BizTalk, SSIS. • Data Warehouse/Data Lake – we have implemented and managed some of the largest data warehouse/data lake infrastructure in the healthcare industry (largest @ 3 petabyte data). • Scorecard/Reports – data visualization for scorecards, dashboards and reports on SharePoint, Roambi, tableau. • Predictive Analytics – build risk stratification and other predictive analytics algorithms using hadoop ecosystem and RStudio.
- 7. 7 DATA PIPELINE EMPI Staging Enterprise Service Bus Data Warehouse BI Analytics, Dashboards, Visualization & Report Coordination Browser Mobile Device Redshift, NoSQL, SQL Server Authorization and DevOpsData Governance Products and Logos acknowledged to respective owners. Logos used to for illus
- 8. 8 ESB VS. INTERFACE ENGINE Courtesy of
- 9. 9 ESB VS. INTERFACE ENGINE Courtesy of
- 10. 10 FINANCIAL FLOW https://www.youtube.com/watch?v=RJKIjxl1KeE Who is paying for your healthcare? Watch the video on the following slide
- 11. 11 CASE STUDIES Retrospective & Predictive
- 12. 12 PREDICTIVE ANALYTICS EXAMPLE –AUTOBED https://www.youtube.com/watch?v=eI1l_s4zo_s Predictive Analytics Example –AutoBed Watch the video on the following Slide
- 14. Know Your Patients Clinical ProtocolsBusiness Intelligence & Predictive Analytics Cohort Identification Standardized Care PathPatient Demographics Lifestyle Medical History Risk Stratification Adverse Event Prediction Family History Proactive Appointments Treatment Effectiveness Patient Generated Data Variance Management Financial Review Patterns Treatment Adjustment Actionable Intelligence POPULATION HEALTH EXPLAINED
- 15. PROTOCOL COMPLIANCE Clinical desktop integration using “Knowledge Hub” Protocol Definition and Matching Clinician view, compliance officer view, reporting moduleLanguage & Terminologies Data Aggregation TouchPoint360 Data existing in hospital systems Calance Protocol Compliance Framework
- 19. IDEAS High Value Questions 19
- 20. 20 AMBULATORY • Is my practice setup to be successful as a Pay-for-Performance contract or should I stay as pay per service? • Should I go an form an Accountable Care Organization (ACO) with 75 other physicians or join an Integrated Delivery Network (IDN)? • What are my compliance requirements with different contracts? • How do I rapidly understand the business implications of (program de jour) for my specific practice so I can rapidly make informed decisions to participate (or not)?
- 21. 21 HIGH ACUITY CARE • What is the probability of patient developing an infection? • Do I have everything for tomorrow? • How are we doing on infection management (SEPSIS)? Is there anything we can do to use the devices to proactive alerts? • What is the correlation between cost index and length of stay?
- 22. 22 DIGITAL PATHOLOGY • Find similar cases • What other information do you need for diagnosis? Additional images, molecular image, additional test results, additional symptoms • How to close the loop between Radiologist and Pathologist to reduce discordance?
- 23. 23 RADIOLOGY • Automatically detect abnormalities in the image • Which Radiologist results in good/bad follow-up actions? • What are my compliance requirements with different contracts? • Is this the right procedure, modality and protocol?
- 24. 24 REVENUE CYCLE • Recommend an immediate step to avoid hospitalization. e.g. cab for picking up Rx • Who is the best provider to treat this patient? • What is the best site for this patient? • Coding accuracy dashboard
Editor's Notes
- Healthcare data is messy – Wide variety of data. Highly specialized software to automate clinical workflows by specialty/department. No two implementations of the same department workflows are same. Too many versions of “standards” for data exchange. HL7 has 30+ variations and none of them are backward compatible. Implementation of interface at every hospital, even within a hospital system, is likely to be different. Think “Misc” field in database; everyone can change the data and structure within a HL7. Unstructured data aka clinical notes. Products from the same vendor does not talk to each other. (Only in the Healthcare industry), the data is owned by the system where it is generated. This means, the developer needs consent and cooperation from the vendor to integrate with their system.
- Data driven initiatives in the healthcare industry are not new. There is no shortage of technology and data in healthcare. Traditional retrospective BI focused on compliance and quality reports. Large hospitals have played with data integration in some form or other for over a decade. Microsoft’s HealthVault, Google Health were exampled of failed attempts at giving patient access to their own health records. Latest in that category is Apple’s HealthKit on iOS devices. Apple HealthKit now supports Continuity of Care Record (CCR) format. HealthVault integrated with home health devices before wearable was a thing – blood glucose meter, pulse oxymeters (for asthma patients), heart rate monitors etc.
- Some technologies you may need to learn if you want to play with healthcare data. Calance is a technology agnostic systems integration firm. We do not endorse any specific product. Tools for retrospective BI are different from predictive analytics. EMPI – Electronic Master Patient Index is widely used to identify and merge patient records from multiple sources into a comprehensive electronic record. Interface engines like Orion Rhapsody and Intersystems’ Ensemble are widely used to deal with different formats and standards. There are open source tools like Talend, Mirth Connect and others that can be used for healthcare interfaces. Columnar databases are better at data warehousing – creating wide views, slicing/dicing massive amounts of data. Focus less on ACID functionality and more on aggregate data performance. Transactional databases are better since they focus on ACID (Atomicity, Consistency, Isolation, Durability) properties. Traditional database management systems e.g. SQL Server, Oracle etc. were hybrid databases attempting to address the needs of both the Transactional systems and the data warehouse. Hybrid databases spend too much administration time on ACID functions and administration, making them bad at large dataset manipulation. Since these DBMS are geared towards structured data, hence the star schemas. Microsoft and Oracle has introduced new columnar databases specifically for data warehousing. Unstructured data e.g. clinical notes is stored differently and manipulated differently. Think about you will manage data quality.
- If you plan to work with healthcare industry, get used to the cash flow. This video explains the relationship between patient (employees), employers, providers (hospital/clinicians) and the payers (insurance companies).
- Because hospitals have so many different moving parts, the cause and effects are rarely in the same department.Here is an example of predictive analytics. In this video, we show how predictive analytics and be used to find the root cause of a problem in surgical ward.
- In the US, there is new push for Accountable Care Organization (ACO) and Affordable Care Act (Obama Care). Hospitals are under huge pressure to change their business model. This slide summarizes what is happening as a results of these new initiatives. Insurance companies are forcing the hospitals from move from a Time and Materials business model to a Fixed Fee business models. aka – “Pay for service” to “Pay for performance” reimbursement model.
- What does Population Health Management means? CRM + Predictive Analytics + Standardized Care paths - variance analysis = actionable intelligence and proactive care
- Example of Calance product – Clinical Protocol Compliance. This product was showcases at HIMSS 2013 at Caradigm booth.
- Predictive analytics based physician dashboard to show “is there anything, I need to do before I go home”.
- Protocol level details and patients who need my (clinicians) attention right now.
- Retrospective dashboard showing variance at aggregate level. Which physicians are using agreed protocols and relatively more efficient?
- Some more ideas for predictive analytics in the subsequent slides. I volunteer office hours at Cambia Grove regularly to help Seattle based healthcare startups. Anyone can book a slot to bounce ideas (for free). Feel free to connect on LinkedIn, @gaurav_kinatus or through Cambia Grove https://www.cambiagrove.com/calendar/all.